{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#载入数据\n",
    "data = np.genfromtxt(\"job.csv\",delimiter = ',')\n",
    "x_data = data[:,0]\n",
    "y_data = data[:,1]\n",
    "plt.scatter(x_data,y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#改变数据维度\n",
    "x_data = data[:,0,np.newaxis]\n",
    "y_data = data[:,1,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#创建并拟合数据\n",
    "model = LinearRegression()\n",
    "model.fit(x_data,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#一元线性回归图\n",
    "plt.plot(x_data,y_data,'b.')\n",
    "plt.plot(x_data,model.predict(x_data),'r.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#定义多项式回归，degree的值可以调节多项式的特征\n",
    "poly_reg = PolynomialFeatures(degree = 5 )\n",
    "#特征处理\n",
    "x_poly = poly_reg.fit_transform(x_data)\n",
    "#定义回归模型\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(x_poly,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00,\n",
       "        1.0000e+00],\n",
       "       [1.0000e+00, 2.0000e+00, 4.0000e+00, 8.0000e+00, 1.6000e+01,\n",
       "        3.2000e+01],\n",
       "       [1.0000e+00, 3.0000e+00, 9.0000e+00, 2.7000e+01, 8.1000e+01,\n",
       "        2.4300e+02],\n",
       "       [1.0000e+00, 4.0000e+00, 1.6000e+01, 6.4000e+01, 2.5600e+02,\n",
       "        1.0240e+03],\n",
       "       [1.0000e+00, 5.0000e+00, 2.5000e+01, 1.2500e+02, 6.2500e+02,\n",
       "        3.1250e+03],\n",
       "       [1.0000e+00, 6.0000e+00, 3.6000e+01, 2.1600e+02, 1.2960e+03,\n",
       "        7.7760e+03],\n",
       "       [1.0000e+00, 7.0000e+00, 4.9000e+01, 3.4300e+02, 2.4010e+03,\n",
       "        1.6807e+04],\n",
       "       [1.0000e+00, 8.0000e+00, 6.4000e+01, 5.1200e+02, 4.0960e+03,\n",
       "        3.2768e+04],\n",
       "       [1.0000e+00, 9.0000e+00, 8.1000e+01, 7.2900e+02, 6.5610e+03,\n",
       "        5.9049e+04],\n",
       "       [1.0000e+00, 1.0000e+01, 1.0000e+02, 1.0000e+03, 1.0000e+04,\n",
       "        1.0000e+05]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图\n",
    "plt.plot(x_data,y_data,'b.')\n",
    "plt.plot(x_data,lin_reg.predict(poly_reg.fit_transform(x_data)),c = 'r')\n",
    "plt.title('Truth  or Bluff (Polynomial Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 由于线是由点组成的\n",
    "# 点太少导致曲线不够平滑\n",
    "\n",
    "#平滑曲线\n",
    "#画图\n",
    "plt.plot(x_data,y_data,'b.')\n",
    "#均匀生成100个点\n",
    "x_test = np.linspace(1,10,100)\n",
    "#改变维度\n",
    "x_test = x_test[:,np.newaxis]\n",
    "plt.plot(x_test,lin_reg.predict(poly_reg.fit_transform(x_test)),c = 'r')\n",
    "plt.title('Truth  or Bluff (Polynomial Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.09090909,  1.18181818,  1.27272727,  1.36363636,\n",
       "        1.45454545,  1.54545455,  1.63636364,  1.72727273,  1.81818182,\n",
       "        1.90909091,  2.        ,  2.09090909,  2.18181818,  2.27272727,\n",
       "        2.36363636,  2.45454545,  2.54545455,  2.63636364,  2.72727273,\n",
       "        2.81818182,  2.90909091,  3.        ,  3.09090909,  3.18181818,\n",
       "        3.27272727,  3.36363636,  3.45454545,  3.54545455,  3.63636364,\n",
       "        3.72727273,  3.81818182,  3.90909091,  4.        ,  4.09090909,\n",
       "        4.18181818,  4.27272727,  4.36363636,  4.45454545,  4.54545455,\n",
       "        4.63636364,  4.72727273,  4.81818182,  4.90909091,  5.        ,\n",
       "        5.09090909,  5.18181818,  5.27272727,  5.36363636,  5.45454545,\n",
       "        5.54545455,  5.63636364,  5.72727273,  5.81818182,  5.90909091,\n",
       "        6.        ,  6.09090909,  6.18181818,  6.27272727,  6.36363636,\n",
       "        6.45454545,  6.54545455,  6.63636364,  6.72727273,  6.81818182,\n",
       "        6.90909091,  7.        ,  7.09090909,  7.18181818,  7.27272727,\n",
       "        7.36363636,  7.45454545,  7.54545455,  7.63636364,  7.72727273,\n",
       "        7.81818182,  7.90909091,  8.        ,  8.09090909,  8.18181818,\n",
       "        8.27272727,  8.36363636,  8.45454545,  8.54545455,  8.63636364,\n",
       "        8.72727273,  8.81818182,  8.90909091,  9.        ,  9.09090909,\n",
       "        9.18181818,  9.27272727,  9.36363636,  9.45454545,  9.54545455,\n",
       "        9.63636364,  9.72727273,  9.81818182,  9.90909091, 10.        ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1,10,100)"
   ]
  }
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